Predicting quantum materials properties using novel faithful machine learning embeddings

TL;DR

This paper introduces new machine learning models and refinements for predicting properties of quantum crystalline materials, achieving state-of-the-art results and facilitating broader application in quantum materials science.

Contribution

The paper presents novel adaptations and refinements of ML networks specifically tailored for quantum materials property prediction, with comprehensive implementation and data handling tools.

Findings

Achieved state-of-the-art accuracy in TQC classification

Performed well in predicting band gaps, magnetic properties, and formation energies

Analyzed dataset errors to identify atypical materials

Abstract

Machine Learning (ML) is accelerating the progress of materials prediction and classification, with particular success in CGNN designs. While classical ML methods remain accessible, advanced deep networks are still challenging to build and train. We introduce two new adaptations and refine two existing ML networks for generic crystalline quantum materials properties prediction and optimization. These new models achieve state-of-the-art performance in predicting TQC classification and strong performance in predicting band gaps, magnetic classifications, formation energies, and symmetry group. All networks easily generalize to all quantum crystalline materials property predictions. To support this, full implementations and automated methods for data handling and materials predictions are provided, facilitating the use of deep ML methods in quantum materials science. Finally, dataset error rates are analyzed using an ensemble model to identify and highlight highly atypical materials for further investigations.